Human analysis on cell size

Load the packages

library(Seurat)
library(data.table)
library(NMF)
library(rsvd)
library(Rtsne)
library(ggplot2)
library(cowplot)
library(sva)
library(igraph)
library(cccd)
library(KernSmooth)
library(beeswarm)
library(stringr)
library(formatR)
source("tools.R")
library(DESeq2)

The function will be used in the follow

Analysis based on cell size

According to the previous analysis on sample group,remove the group hc001 and cell size 2um ### Read data ### Data QA

human.only.pro <- Load_data(data_dir = "../data/human.txt")
important.genes <- c("ITGB4", "ABCB5", "KRT19", "ACTB", "KRT12", "KRT5", "GAPDH", 
    "KRT3", "PAX6", "WNT7A", "KRT14", "TP63", "KRT10")
human.only.pro <- human.only.pro[, colnames(human.only.pro)[unlist(lapply(colnames(human.only.pro), 
    function(x) return(str_split(x, "_")[[1]][2]))) %in% c("10um", "20um", "6um")]]
human.only.pro <- human.only.pro[, colnames(human.only.pro)[!unlist(lapply(colnames(human.only.pro), 
    function(x) return(str_split(x, "_")[[1]][1]))) %in% "hc001"]]

Create Seurat object and not caculate DESeq,but not set min.cells and min.genes

# only select the cells contain 10 genes expressed at least,select the genes
# must be expressed in two cells at least
human.all.DESeq <- DESeq_SeuratObj(X = human.only.pro, DESq = FALSE, min.cells = 10, 
    min.genes = 2)

## [1] "Scaling data matrix"
## 
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all.sample.group <- unlist(lapply(human.all.DESeq@cell.names, function(x) return(str_split(x, 
    "_")[[1]][1])))
all.sample.size <- unlist(lapply(human.all.DESeq@cell.names, function(x) return(str_split(x, 
    "_")[[1]][2])))
# reset ident
human.all.DESeq <- SetIdent(human.all.DESeq, cells.use = human.all.DESeq@cell.names, 
    ident.use = all.sample.size)

Figure Explore

First,use the plot,eg. Barplot,Violin…,we can explore some message from sample

Group_Bar(human.all.DESeq@raw.data, group = all.sample.group)

Group_Bar(human.all.DESeq@raw.data, group = all.sample.size)

# We are interested in the gene ITGB4
GenePlot(human.all.DESeq, gene1 = "ITGB4", gene2 = important.genes[2])
# VlnPlot(human.all.DESeq,features.plot = 'ITGB4',y.lab.rot = 90) # Violinn
# plot of gene ITGB in all sample
VlnPlot(human.all.DESeq, features.plot = important.genes[important.genes %in% 
    rownames(human.all.DESeq@raw.data)], y.lab.rot = 90)  # Violinn plot of gene ITGB in all sample

Dimensionality reduction

PCA and tSNE

Here,do the dimensionality reduction using the PCA, tSNE method 

all.pbmc <- PCA.TSNE(object = human.all.DESeq, pcs.compute = FALSE, num.pcs = 28)

After the PCA and tSNE,try plot: Featureplot of ITGB4,four var.genes,PCA plot,tSNE plot…

# FeaturePlot(object = all.pbmc,features.plot ='ITGB4',pt.size = 4,no.legend
# = FALSE) # ITGB4 gene in part dataset
FeaturePlot(object = all.pbmc, features.plot = important.genes[important.genes %in% 
    rownames(human.all.DESeq@raw.data)], pt.size = 1, no.legend = FALSE, reduction.use = "pca")  # ITGB4 gene in part dataset

DimPlot(all.pbmc, reduction.use = "tsne", pt.size = 4)  #  grour by sample

DimPlot(all.pbmc, reduction.use = "pca", pt.size = 4)  #  grour by sample

DimHeatmap(all.pbmc, reduction.type = "pca", check.plot = FALSE)

FeatureHeatmap(all.pbmc, features.plot = "ITGB4", pt.size = 3, plot.horiz = TRUE, 
    cols.use = c("lightgrey", "blue"))

The Faetureplot of ITGB4, ABCB5, KRT19, ACTB, KRT12, KRT5, GAPDH, KRT3, PAX6, WNT7A, KRT14, TP63, KRT10based on PCA shows that,they only has high expression level in few samples,and expresss lowly in most sample.It means that may be these important genes express differently across sample.The plot also tell us the gene KRT5,GAPDH,PAXX6,KRT14 have more higher expression level than the other important genes.It is consistent with the result of violin plot. About the heatmap,we only show the gene ITGB4 And the FeatureHeatmap and Heamap also comfirm this phenomeno.We try the other four variable genes,which has the similar result as gene ITGB4 But the tSNE and * PCA * plot show that, the sample can not be split apparently.The result may be is not good based on the PCA and tSNE method.

Differential expression

Next,we will have analysis on gene differential expression.Find maker genes across sample.We use the method: **wilcox test**

# Finds markers (differentially expressed genes) for each of the identity
# classes in a dataset
all_markers <- FindAllMarkers(all.pbmc, test.use = "bimod", print.bar = FALSE)
head(all_markers)

##                       p_val  avg_logFC pct.1 pct.2     p_val_adj cluster
## RP11-217O12.1 2.160836e-158  2.8858644 0.994 0.971 4.434900e-154     6um
## AC009501.4    5.012946e-123  3.0201807 0.724 0.231 1.028857e-118     6um
## ACTG1P12       4.280359e-95  0.4914296 0.051 0.215  8.785009e-91     6um
## PRRG3          3.425743e-69  0.9104018 0.179 0.118  7.030996e-65     6um
## CYP24A1        2.304897e-64  2.2032348 0.667 0.118  4.730571e-60     6um
## MT-CO2         2.847717e-58 -1.7518920 0.872 0.981  5.844655e-54     6um
##                        gene
## RP11-217O12.1 RP11-217O12.1
## AC009501.4       AC009501.4
## ACTG1P12           ACTG1P12
## PRRG3                 PRRG3
## CYP24A1             CYP24A1
## MT-CO2               MT-CO2

We check whether the important genes are still in the marker genes we found from the DESeq analysis. the genes:ITGB4, KRT19, ACTB, KRT5, GAPDH, KRT3, PAX6, KRT14 are still in the marker genes.

Bar plot of gene’s p.val

human.heatmap <- Heatmap_fun(genes = important.genes[important.genes %in% rownames(human.all.DESeq@raw.data)], 
    tpm.data = all.pbmc@scale.data, condition = unique(as.character(all.pbmc@ident)), 
    all.condition = as.character(all.pbmc@ident))

## There ara 3 conditions
## Whether creat data accurate 0

NMF::aheatmap(human.heatmap[[2]], Rowv = NA, Colv = NA, annCol = human.heatmap[[1]], 
    scale = "none")

We have find all marker genes across sample,there are 2322 significant genes(adjust p-value <0.05) in all marker genes.

Next,Spectral k-means clustering on single cells based on PCA

all.pbmc <- KClustDimension(all.pbmc, reduction.use = "pca", k.use = 3)
clusters.pca <- all.pbmc@meta.data$kdimension.ident
DimPlot(all.pbmc, pt.size = 4, group.by = "kdimension.ident")

Spectral k-means clustering on single cells based on tSNE

all.pbmc <- KClustDimension(all.pbmc, reduction.use = "tsne", k.use = 3)
clusters.tsne <- all.pbmc@meta.data$kdimension.ident
DimPlot(all.pbmc, pt.size = 4, group.by = "kdimension.ident", reduction.use = "tsne")

Differential expression.

When use the DESeq,it must require the gene count matrix satisify that: every gene contains at least one zero, cannot compute log geometric means. So have to take another method to handle data,but I do not know whether it is reasonable.Just try!!!

condition.1 <- unlist(lapply(all.pbmc@cell.names, function(x) return(str_split(x, 
    "_")[[1]][2])))
# xdds<-DESeq_CT(count.data = all.pbmc@raw.data,condition.1 = condition.1)
load("Human.cellsize.RData")
plotDispEsts(xdds, main = "Per-gene Dispersion")

Do the DESeq test across all cells with sample group.And get all the significant genes between two groups(p.value < 0.05)

r <- DESeq_result(xdds, condition = condition.1)

load("Human.cellsize.genes.RData")
x <- as.vector(r)
library(VennDiagram)
grid.draw(venn.diagram(x[1:3], filename = NULL, fill = c("dodgerblue", "goldenrod1", 
    "darkorange1")))